Machine learning classifiers are used in conjunction with a variety of technologies. In one aspect of machine learning, a model is constructed using a set of training data (also referred to as example data). Once constructed, the model can be used to classify other data (e.g., data not included in the set of training data). Unfortunately, models are imperfect, and data will sometimes be incorrectly classified using such models. Determining why a model has yielded an incorrect classification can be very difficult. Accordingly, improvements in techniques for building, evaluating, and refining models would be desirable.